Clinical-Pharmacogenetic Predictive Models for Time to Occurrence of Levodopa Related Motor Complications in Parkinson’s Disease

The response to dopaminergic treatment in Parkinson’s disease depends on many clinical and genetic factors. The very common motor fluctuations (MF) and dyskinesia affect approximately half of patients after 5 years of treatment with levodopa. We did an evaluation of a combined effect of 16 clinical parameters and 34 single nucleotide polymorphisms to build clinical and clinical-pharmacogenetic models for prediction of time to occurrence of motor complications and to compare their predictive abilities. In total, 220 Parkinson’s disease patients were included in the analysis. Their demographic, clinical, and genotype data were obtained. The combined effect of clinical and genetic factors was assessed using The Least Absolute Shrinkage and Selection Operator penalized regression in the Cox proportional hazards model. Clinical and clinical-pharmacogenetic models were constructed. The predictive capacity of the models was evaluated with the cross-validated area under time-dependent receiver operating characteristic curve. Clinical-pharmacogenetic model included age at diagnosis (HR = 0.99), time from diagnosis to initiation of levodopa treatment (HR = 1.24), COMT rs165815 (HR = 0.90), DRD3 rs6280 (HR = 1.03), and BIRC5 rs9904341 (HR = 0.95) as predictive factors for time to occurrence of MF. Furthermore, clinical-pharmacogenetic model for prediction of time to occurrence of dyskinesia included female sex (HR = 1.07), age at diagnosis (HR = 0.97), tremor-predominant Parkinson’s disease (HR = 0.88), beta-blockers (HR = 0.95), alcohol consumption (HR = 0.99), time from diagnosis to initiation of levodopa treatment (HR = 1.15), CAT rs1001179 (HR = 1.27), SOD2 rs4880 (HR = 0.95), NOS1 rs2293054 (HR = 0.99), COMT rs165815 (HR = 0.92), and SLC22A1 rs628031 (HR = 0.80). Areas under the curves for clinical and clinical-pharmacogenetic models for MF after 5 years of levodopa treatment were 0.68 and 0.70, respectively. Areas under the curves for clinical and clinical-pharmacogenetic models for dyskinesia after 5 years of levodopa treatment were 0.71 and 0.68, respectively. These results show that clinical-pharmacogenetic models do not have better ability to predict time to occurrence of motor complications in comparison to the clinical ones despite the significance of several polymorphisms. Models could be improved by a larger sample size and by additional polymorphisms, epigenetic predictors or serum biomarkers

Different approaches in microRNA analysis

MicroRNA might serve as a predictive biomarker for treatment response in stem cell treatment in knee osteoarthritis. Different sample types are going to be collected to enlighten the true biological role. MicroRNA analysis necessitates diverse approaches based on the sample type. In this study, we examined microRNA profiles in plasma samples, synovial fluid, and adipose-derived fat tissue. We conducted a comparative analysis of different microRNA analysis methods to assess the data. The first approach involved a series of steps, including adapter trimming, quality filtering, size filtering, and mapping of all reads to the human reference genome (GRCh38.p12). Subsequently, genome-mapped reads were aligned to known miRNA sequences from miRBase. Reads that did not match miRNAs were subjected to further classification using additional databases, such as RNAcentral. The second pipeline also encompassed adapter trimming, quality filtering, and size filtering. Additionally, it involved collapsing individual reads into repeat sequences, followed by alignment to the mature index of miRBase. Unaligned reads were classified as isomiRs based on their alignment to the hairpin index of miRBase. We processed sequences from three plasma samples, three adipose fat tissue samples, and three synovial fluid samples. Although there were slight variations in microRNA read counts, the average ratio between counts was 0.92 (SD=0.29). Notably, the second pipeline yielded higher read counts compared to the first pipeline. The results obtained from both microRNA bioinformatic pipelines demonstrated similar outcomes, suggesting that the choice of pipeline is unlikely to have a significant impact on the derived biological insights.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202

A multinational prospective observational real-world cohort study

Funding Information: The authors thank the ISPAD executive committee and ISPAD JENIOUS members for their support. An abstract with partial study data was presented in June 2021 at the Virtual Advanced Technologies and Treatment for Diabetes (ATTD) conference. There was no commercial sponsor for this study. This study was partially funded by the ISPAD JDRF Fellowship Grant. KD was supported by the Slovenian National Research Agency (grant nos. J3–6798, V3–1505, and P3–0343). Funding Information: ISPAD, Grant/Award Number: ISPAD JDRF Fellowship Grant; Slovenian National Research Agency, Grant/Award Numbers: J36798, V31505, P30343 Funding information Funding Information: KD received honoraria for participation on the speakerʼs bureau of Pfizer, Novo Nordisk, and Eli Lilly. JG received speakerʼs honoraria from Eli Lilly and Sanofi, and clinical trials investigatorʼs payment from Novo Nordisk. RM received advisory board honoraria from Abbott and Novo Nordisk. JP received speakerʼs honoraria from Medtronic. JS serves as a consultant to Cecelia Health, Lexicon, Lilly, Insulet, Medtronic, and Sanofi, is a member of the advisory board for Bigfoot Biomedical, Cecelia Health, Insulet, Medtronic, and the T1D Fund and Vertex, and has had research support from the NIH, JDRF, and the Helmsley Charitable Trust. Her institution has had research support from Medtronic and Insulet. AC received speakerʼs honoraria from Medtronic, Eli Lilly, and Novo Nordisk. TB received speakerʼs honoraria from DexCom, Medtronic, Novo Nordisk, Roche, Sanofi, and Ypsomed, and advisory board honoraria from Ascensia, AstraZeneca, DexCom, Medtronic, and Sanofi.publishersversionpublishe

Continuous glucose monitoring use and glucose variability in very young children with type 1 diabetes ( VibRate ): A multinational prospective observational real‐world cohort study

info:eu-repo/semantics/publishedVersio

Familial hypercholesterolaemia in children and adolescents from 48 countries: a cross-sectional study

Background: Approximately 450 000 children are born with familial hypercholesterolaemia worldwide every year, yet only 2·1% of adults with familial hypercholesterolaemia were diagnosed before age 18 years via current diagnostic approaches, which are derived from observations in adults. We aimed to characterise children and adolescents with heterozygous familial hypercholesterolaemia (HeFH) and understand current approaches to the identification and management of familial hypercholesterolaemia to inform future public health strategies. Methods: For this cross-sectional study, we assessed children and adolescents younger than 18 years with a clinical or genetic diagnosis of HeFH at the time of entry into the Familial Hypercholesterolaemia Studies Collaboration (FHSC) registry between Oct 1, 2015, and Jan 31, 2021. Data in the registry were collected from 55 regional or national registries in 48 countries. Diagnoses relying on self-reported history of familial hypercholesterolaemia and suspected secondary hypercholesterolaemia were excluded from the registry; people with untreated LDL cholesterol (LDL-C) of at least 13·0 mmol/L were excluded from this study. Data were assessed overall and by WHO region, World Bank country income status, age, diagnostic criteria, and index-case status. The main outcome of this study was to assess current identification and management of children and adolescents with familial hypercholesterolaemia. Findings: Of 63 093 individuals in the FHSC registry, 11 848 (18·8%) were children or adolescents younger than 18 years with HeFH and were included in this study; 5756 (50·2%) of 11 476 included individuals were female and 5720 (49·8%) were male. Sex data were missing for 372 (3·1%) of 11 848 individuals. Median age at registry entry was 9·6 years (IQR 5·8-13·2). 10 099 (89·9%) of 11 235 included individuals had a final genetically confirmed diagnosis of familial hypercholesterolaemia and 1136 (10·1%) had a clinical diagnosis. Genetically confirmed diagnosis data or clinical diagnosis data were missing for 613 (5·2%) of 11 848 individuals. Genetic diagnosis was more common in children and adolescents from high-income countries (9427 [92·4%] of 10 202) than in children and adolescents from non-high-income countries (199 [48·0%] of 415). 3414 (31·6%) of 10 804 children or adolescents were index cases. Familial-hypercholesterolaemia-related physical signs, cardiovascular risk factors, and cardiovascular disease were uncommon, but were more common in non-high-income countries. 7557 (72·4%) of 10 428 included children or adolescents were not taking lipid-lowering medication (LLM) and had a median LDL-C of 5·00 mmol/L (IQR 4·05-6·08). Compared with genetic diagnosis, the use of unadapted clinical criteria intended for use in adults and reliant on more extreme phenotypes could result in 50-75% of children and adolescents with familial hypercholesterolaemia not being identified. Interpretation: Clinical characteristics observed in adults with familial hypercholesterolaemia are uncommon in children and adolescents with familial hypercholesterolaemia, hence detection in this age group relies on measurement of LDL-C and genetic confirmation. Where genetic testing is unavailable, increased availability and use of LDL-C measurements in the first few years of life could help reduce the current gap between prevalence and detection, enabling increased use of combination LLM to reach recommended LDL-C targets early in life

Clinical and clinical-pharmacogenetic models for prediction of the most common psychiatric complications due to dopamingeric treatment in Parkinson\u27s disease

Background: The most common psychiatric complications due to dopaminergic treatment in Parkinson\u27s disease are visual hallucinations and impulse control disorders. Their development depends on clinical and genetic factors. Methods: We evaluated the simultaneous effect of 16 clinical and 34 genetic variables on the occurrence of visual hallucinations and impulse control disorders. Altogether, 214 Parkinson\u27s disease patients were enrolled. Their demographic, clinical, and genotype data were obtained. Clinical and clinical-pharmacogenetic models were built by The Least Absolute Shrinkage and Selection Operator penalized logistic regression. The predictive capacity was evaluated with the cross-validated area under the receiver operating characteristic curve (AUC). Results: The clinical-pharmacogenetic index for prediction of visual hallucinations encompassed age at diagnosis (OR = 0.99), rapid eye movement (REM) sleep behavior disorder (OR = 2.27), depression (OR = 1.0002), IL6 rs1800795 (OR = 0.99), GPX1s1050450 (OR = 1.07), COMT rs165815 (OR = 0.69), MAOB rs1799836 (OR = 0.97), DRD3 rs6280 (OR = 1.32), and BIRC5 rs8073069 (OR = 0.94). The clinical-pharmacogenetic index for prediction of impulse control disorders encompassed age at diagnosis (OR = 0.95), depression (OR = 1.75), beta-blockers (OR = 0.99), coffee consumption (OR = 0.97), NOS1 rs2682826 (OR = 1.15), SLC6A3rs393795 (OR = 1.27), SLC22A1 rs628031 (OR = 1.19), DRD2 rs1799732 (OR = 0.88), DRD3 rs6280 (OR = 0.88), and NRG1 rs3924999 (OR = 0.96). The cross-validated AUCs of clinical and clinical-pharmacogenetic models for visual hallucinations were 0.60 and 0.59, respectively. The AUCs of clinical and clinical-pharmacogenetic models for impulse control disorders were 0.72 and 0.71, respectively. The AUCs show that the addition of selected genetic variables to the analysis does not contribute to better prediction of visual hallucinations and impulse control disorders. Conclusions: Models could be improved by a larger cohort and by addition of other types of Parkinson\u27s disease biomarkers to the analysis

Focused peptide library screening as a route to a superior affinity ligand for antibody purification

Affinity chromatography is the linchpin of antibody downstream processing and typically relies on bacterial immunoglobulin (Ig)-binding proteins, epitomized by staphylococcal protein A-based ligands. However, such affinity ligands are fairly costly and suffer from chemical instability, leading to ligand denaturation and leaching from chromatographic support. Innovations in this area are aimed at developing robust and highly selective antibody ligands capable of withstanding harsh column sanitization conditions. We report the development and first-stage characterization of a selective short linear peptide ligand of the IgG Fc region capable of capturing all four IgG subclasses. The ligand was discovered through in vitro directed evolution. A focused phage-display library based on a previously identified peptide lead was subjected to a single-round screen against a pool of human IgG. The hits were identified with next-generation sequencing and ranked according to the enrichment ratio relative to their frequency in the pre-screened library. The top enriched peptide GSYWYNVWF displaying highest affinity for IgG was coupled to bromohydrin-activated agarose beads via a branched linker. The resulting affinity matrix was characterized with a dynamic binding capacity of approx. 43 mg/mL, on par with commercially employed protein A-based resin

Detection of metabolic syndrome burden in healthy young adults may enable timely introduction of disease prevention

ntroduction: Metabolic syndrome and associated diseases are a global health problem. Detection of early metabolic modifications that may lead to metabolic syndrome would enable timely introduction of preventive lifestyle modifications. Material and methods: In total 103 young, healthy adults were assessed for indicators of metabolic alterations. Anthropometric, lifestyle, genetic and biochemical parameters were assessed. Individuals who fulfilled at least one criterion for diagnosis of metabolic syndrome were assigned to the group with the higher metabolic syndrome burden (B-MeS). Results: The 34 young healthy individuals who were assigned to the B-MeS group had lower fat-free mass, higher body mass index, waist-to-hip ratio, fat mass, and blood pressure, more visceral fat, they were less physically ac-tive, had higher C-reactive protein values and higher catalase activity. Their phenotype was more similar to that of patients diagnosed with metabolic syndrome than the rest of the population. Conclusions: Simple anthropometric measurements, lifestyle assessment and basic biochemical measurements can be used to identify young healthy individuals with increased risk for metabolic syndrome. These assessments can be performed at periodic check-ups of the healthy population so that timely diagnosis of B-MeS can be made. As lifestyle factors have a big influ-ence on development or improvement of the MeS, the timely diagnosis for B-MeS would enable an early opportunity for intervention for lifestyle mod-ification in the still healthy population, saving costs and reducing disability adjusted life years

Clinical pharmacogenetic models of treatment response to methotrexate monotherapy in Slovenian and Serbian rheumatoid arthritis patients

Objectives: Methotrexate (MTX) is the first line treatment for rheumatoid arthritis (RA), but nevertheless 30% of patients experience MTX inefficacy. Our aim was to develop a clinical pharmacogenetic model to predict which RA patients will not respond to MTX monotherapy. We also assessed whether this model can be generalized to other populations by validating it on a group of Serbian RA patients. Methods: In 110 RA Slovenian patients, data on clinical factors and 34 polymorphisms in MTX pathway were analyzed by Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression to select variables associated with the disease activity as measured by Disease Activity Score (DAS28) score after 6 months of MTX monotherapy. A clinical pharmacogenetic index was constructed from penalized regression coefficients with absolute value above 0.05. This index was cross-validated and also independently validated on 133 Serbian RA patients. Results: A clinical pharmacogenetic index for prediction of DAS28 after 6 months of MTX monotherapy in Slovenian RA patients consisted of DAS28 score at diagnosis, presence of erosions, MTX dose, Solute Carrier Family 19 Member 1 (SLC19A1) rs1051266, Solute Carrier Organic Anion Transporter Family Member 1B1 (SLCO1B1) rs2306283, Thymidylate Synthase (TYMS), and Adenosine Monophosphate Deaminase 1 (AMPD1) rs17602729. It correctly classified 69% of Slovenian patients as responders or nonresponders and explained 30% of variability in DAS28 after 6 months of MTX monotherapy. Testing for validity in another population showed that it classified correctly only 22.5% of Serbian RA patients. Conclusions: We developed a clinical pharmacogenetic model for DAS28 after 6 months of MTX monotherapy in Slovenian RA patients by combining clinical and genetic variables. The clinical pharmacogenetic index developed for Slovenian patients did not perform well on Serbian patients, presumably due to the differences in patients\u27 characteristics and clinical management between the two groups